• Shenglong Lin, Xiangmei Wang, Zixin Xu, Lu Li, Rui Kang, Songtao Li, Xiaoyong Wu, Yueyong Zhu, and Haibing Gao.
• Department of Severe Hepatopathy, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian Province, China.
• Ann. Med. 2024 Dec 1; 56 (1): 24104032410403.

ObjectiveHepatic encephalopathy (HE) is a serious complication of acute-on-chronic liver failure (ACLF) that requires early detection and intervention to positively impact patient prognosis. This study aimed to develop a reliable model to predict HE in ACLF patients during hospitalization.MethodsRetrospectively recruiting 255 hepatitis B-related ACLF patients, including 67 who developed HE during hospitalization, the study analysed clinical data and biochemical indices collected during the first week of admission. The least absolute shrinkage and selection operator (LASSO) was used to identify characteristic predictors for hospitalization HE events, and a logistic regression model was subsequently developed. Receiver operating characteristic (ROC) curves, calibration curves, and bootstrap resampling were used to evaluate the model's discrimination, consistency, and accuracy, and a nomogram was created to visualize the model. An external validation cohort of 236 liver failure patients collected from the same medical centre between 2007 and 2010 was used to validate the model.ResultsThe study found that blood urea nitrogen (BUN), alpha-fetoprotein (AFP), international normalized ratio (INR), serum ammonia, and infection complications during hospitalization were risk factors for HE in ACLF patients. The new model predicted the development of HE in ACLF patients with an area under the receiver operating characteristic curve (AUROC) of 85.2%, which was superior to other models. The best threshold for the new model was 0.28, resulting in a specificity of 81.4% and a sensitivity of 80.6%. In the validation group, the new model showed similar results, with an AUROC of 79% and a specificity of 83.6% and a sensitivity of 56.6%.ConclusionThis study developed and validated a new prediction model for HE in ACLF patients offering a useful tool for early identification of patients with a high risk of HE in clinical settings. However, to ascertain the model's general effectiveness, future prospective multicentre studies are warranted.

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